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AI​/ML Engineer

Job in Reston, Fairfax County, Virginia, 22090, USA
Listing for: Interon IT Solutions
Full Time position
Listed on 2026-03-12
Job specializations:
  • Software Development
    AI Engineer, Machine Learning/ ML Engineer, Data Engineer, Python
Salary/Wage Range or Industry Benchmark: 80000 - 100000 USD Yearly USD 80000.00 100000.00 YEAR
Job Description & How to Apply Below

Job Title: AI/ML Engineer (Python, AWS, GenAI)

Location: Reston, VA (In-person interviews required)

Candidate Requirement: Local to East Coast only

Job Description

Client is seeking an AI/ML Engineer to design and deliver end-to-end AI/ML and Generative AI solutions in a cloud-native AWS environment. The ideal candidate will have strong Python engineering skills, experience deploying ML/GenAI workloads on AWS, and hands‑on expertise building RAG pipelines and MLOps workflows.

Responsibilities
  • Design and implement end-to-end AI/ML and Generative AI solutions using Python, including training, evaluation, optimization, and deployment.
  • Build and maintain cloud-native applications on AWS using Lambda, ECS/Fargate, S3, API Gateway, Dynamo

    DB, RDS/Aurora, Sage Maker, and Bedrock.
  • Develop scalable, high-performance Python microservices using FastAPI/Flask for data pipelines, real-time analytics, and model inference.
  • Architect and operationalize RAG pipelines, embeddings, vector databases, and LLM-powered solutions (chatbots, summarization, semantic search, anomaly detection).
  • Implement CI/CD pipelines (Git Hub/Git Lab/Code Pipeline) and Infrastructure-as-Code using Terraform/Cloud Formation.
  • Build and support MLOps workflows: model versioning, containerized training/inference, automated retraining, monitoring, and performance tuning.
Required Skills
  • Strong hands-on experience in Python development for AI/ML and GenAI solutions.
  • Proven experience working with AWS services for application and ML deployments.
  • Experience building APIs/microservices using FastAPI or Flask.
  • Hands‑on experience with RAG, embeddings, and vector databases.
  • Experience with CI/CD and IaC (Terraform/Cloud Formation).
  • Strong understanding of MLOps practices and production model monitoring.
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